{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a2a2f94a-1a70-43b2-9322-ecd4df6b236e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/xdchen/Softwares/miniforge3/envs/py312-E2S/lib/python3.12/site-packages/pyproj/network.py:59: UserWarning: pyproj unable to set PROJ database path.\n",
      "  _set_context_ca_bundle_path(ca_bundle_path)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import scipy.io as sio\n",
    "\n",
    "import random\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "from torch.utils.data import TensorDataset, DataLoader\n",
    "\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import PlotFuncsXC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "794292c4-b5a4-4138-ba39-b9d708b4675f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<torch._C.Generator at 0x7f0fb4340b50>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#seed = 42\n",
    "seed = 100\n",
    "random.seed(seed)\n",
    "torch.manual_seed(seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f07396e5-15f7-42f2-bef6-e70a250048e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def manual_train_test_split(X, y, test_size=0.2, random_state=None):\n",
    "    \"\"\"\n",
    "    A lightweight alternative to sklearn's train_test_split.\n",
    "    \"\"\"\n",
    "    if random_state:\n",
    "        np.random.seed(random_state)\n",
    "    \n",
    "    # Ensure we are working with numpy arrays\n",
    "    X = np.array(X)\n",
    "    y = np.array(y)\n",
    "    \n",
    "    # Create an array of indices and shuffle them\n",
    "    indices = np.arange(X.shape[0])\n",
    "    np.random.shuffle(indices)\n",
    "    \n",
    "    # Calculate the split point\n",
    "    split_idx = int(len(indices) * (1 - test_size))\n",
    "    \n",
    "    # Slice the indices\n",
    "    train_idx, test_idx = indices[:split_idx], indices[split_idx:]\n",
    "    \n",
    "    # Return the split data\n",
    "    return X[train_idx], X[test_idx], y[train_idx], y[test_idx]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fe516d4c-cae0-4cd3-802c-927d263b34ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "rootdir = '/raid1/xdchen/collaborations/Pei/NN_type/'\n",
    "datadir = rootdir + 'data/'\n",
    "plotdir = rootdir + 'plots/'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c716c0e4-bc83-4621-9cb4-6a04f1472deb",
   "metadata": {},
   "source": [
    "## 1. load data, shuffle it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cde6cc9c-15bb-4299-b991-1e1ab23dc8df",
   "metadata": {},
   "outputs": [],
   "source": [
    "infile = datadir + 'fulldata_8type.mat'\n",
    "Xdata_full = sio.loadmat(infile)['X_full']\n",
    "ydata_full_raw = sio.loadmat(infile)['y_full'][0]-1\n",
    "\n",
    "ydata_full = np.zeros(ydata_full_raw.shape[0])\n",
    "ydata_full[ydata_full_raw==0] = 0\n",
    "ydata_full[ydata_full_raw==1] = 1\n",
    "ydata_full[ydata_full_raw==2] = 2\n",
    "ydata_full[ydata_full_raw==3] = 4\n",
    "ydata_full[ydata_full_raw==4] = 5\n",
    "ydata_full[ydata_full_raw==5] = 6\n",
    "ydata_full[ydata_full_raw==6] = 7\n",
    "ydata_full[ydata_full_raw==7] = 3\n",
    "#sio.savemat(outfile, {'X_full':X_full, 'y_full':y_full})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "69597c6e-8bc8-451d-953e-39d7dde8de1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "valid_index = (ydata_full==0)|(ydata_full==1)|(ydata_full==2)|(ydata_full==3)\n",
    "ydata_full = ydata_full[valid_index]\n",
    "Xdata_full = Xdata_full[valid_index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4176c1d5-1f96-45f5-b868-0930ea56d881",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "625\n"
     ]
    }
   ],
   "source": [
    "nrec = Xdata_full.shape[0]\n",
    "print(nrec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d9d4612f-d831-4d83-948a-af26c09efc73",
   "metadata": {},
   "outputs": [],
   "source": [
    "Xdata_rnd = np.zeros((nrec, 201))\n",
    "ydata_rnd = np.zeros(nrec)\n",
    "\n",
    "nums = random.sample(range(nrec), nrec)\n",
    "Xdata_rnd = Xdata_full[nums]\n",
    "ydata_rnd = ydata_full[nums]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f59843ff-b956-46ee-9ce6-3fc11de33c69",
   "metadata": {},
   "outputs": [],
   "source": [
    "tt_ratio = 0.8\n",
    "Xdata_dev, Xdata_eval, ydata_dev, ydata_eval = manual_train_test_split(Xdata_rnd, ydata_rnd, test_size=1-tt_ratio, random_state=seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "77207021-42e5-4e1f-9999-a4b90ed727dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = torch.tensor(Xdata_dev, dtype=torch.float32)\n",
    "y = torch.tensor(ydata_dev, dtype=torch.long)\n",
    "\n",
    "N = X.size(0)\n",
    "perm = torch.randperm(N)\n",
    "split = int(tt_ratio * N)\n",
    "idx_train, idx_val = perm[:split], perm[split:]\n",
    "X_train, y_train = X[idx_train], y[idx_train]\n",
    "X_val,   y_val   = X[idx_val],   y[idx_val]\n",
    "\n",
    "train_ds = TensorDataset(X_train, y_train)\n",
    "val_ds   = TensorDataset(X_val,   y_val)\n",
    "\n",
    "train_loader = DataLoader(train_ds, batch_size=16, shuffle=True)\n",
    "val_loader   = DataLoader(val_ds,   batch_size=16, shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "84627d24-5837-4ddb-8fbd-40ee02856279",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(62)\n",
      "tensor(110)\n",
      "tensor(172)\n",
      "tensor(56)\n",
      "tensor(0)\n",
      "tensor(0)\n",
      "tensor(0)\n",
      "tensor(0)\n"
     ]
    }
   ],
   "source": [
    "for i in np.arange(8):\n",
    "    print((y_train==i).sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8954fc4a-938d-40c2-9a06-ecea9053eec5",
   "metadata": {},
   "source": [
    "## 2. get the NN model up and running"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "eca0e818-3595-4a2d-b58b-916549ca76e0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cuda\n"
     ]
    }
   ],
   "source": [
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "print(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "81d88fe6-05a6-4852-81dc-fbc35d79595b",
   "metadata": {},
   "outputs": [],
   "source": [
    "class DenseNet(nn.Module):\n",
    "    def __init__(self, in_dim=201, num_classes=4):\n",
    "        super().__init__()\n",
    "        self.net = nn.Sequential(\n",
    "            nn.Linear(in_dim, 64),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(64, num_classes)  # raw logits\n",
    "        )\n",
    "    def forward(self, x):\n",
    "        return self.net(x)\n",
    "\n",
    "model = DenseNet().to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a05dd565-c13f-45a2-ae40-cc76c5fe96c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch   0 | Val acc: 0.600\n",
      "Epoch  20 | Val acc: 0.770\n",
      "Epoch  40 | Val acc: 0.990\n",
      "Epoch  60 | Val acc: 0.990\n",
      "Epoch  80 | Val acc: 0.990\n",
      "Epoch 100 | Val acc: 1.000\n",
      "Epoch 120 | Val acc: 1.000\n",
      "Epoch 140 | Val acc: 1.000\n",
      "\n",
      "Validation accuracy: 1.0\n",
      "Confusion matrix (rows=true, cols=pred):\n",
      " [[18  0  0  0]\n",
      " [ 0 30  0  0]\n",
      " [ 0  0 41  0]\n",
      " [ 0  0  0 11]]\n"
     ]
    }
   ],
   "source": [
    "# -----------------------------\n",
    "# Loss & optimizer\n",
    "# -----------------------------\n",
    "criterion = nn.CrossEntropyLoss()  # expects (N,C) logits and (N,) long labels\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)\n",
    "epochs = 150\n",
    "\n",
    "# -----------------------------\n",
    "# Training loop\n",
    "# -----------------------------\n",
    "def evaluate(loader):\n",
    "    model.eval()\n",
    "    correct, total = 0, 0\n",
    "    # Confusion matrix 4x4\n",
    "    cm = torch.zeros(4, 4, dtype=torch.int64)\n",
    "    with torch.no_grad():\n",
    "        for xb, yb in loader:\n",
    "            xb, yb = xb.to(device), yb.to(device)\n",
    "            logits = model(xb)\n",
    "            preds = torch.argmax(logits, dim=1)\n",
    "            correct += (preds == yb).sum().item()\n",
    "            total += yb.size(0)\n",
    "            for t, p in zip(yb.view(-1), preds.view(-1)):\n",
    "                cm[t.long(), p.long()] += 1\n",
    "    acc = correct / max(total, 1)\n",
    "    return acc, cm\n",
    "\n",
    "train_progress_matrix = np.zeros((epochs, 2))\n",
    "\n",
    "for epoch in range(epochs):\n",
    "    model.train()\n",
    "    for xb, yb in train_loader:\n",
    "        xb, yb = xb.to(device), yb.to(device)\n",
    "        logits = model(xb)\n",
    "        loss = criterion(logits, yb)\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "    \n",
    "    if epoch % 5 == 0 or epoch == 1:\n",
    "        train_acc, _ = evaluate(train_loader)\n",
    "        train_progress_matrix[epoch,0] = train_acc\n",
    "        val_acc, _ = evaluate(val_loader)\n",
    "        train_progress_matrix[epoch,1] = val_acc\n",
    "        if epoch % 20 == 0:\n",
    "            print(f\"Epoch {epoch:3d} | Val acc: {val_acc:.3f}\")\n",
    "\n",
    "# -----------------------------\n",
    "# Final evaluation\n",
    "# -----------------------------\n",
    "val_acc, cm = evaluate(val_loader)\n",
    "print(\"\\nValidation accuracy:\", round(val_acc, 3))\n",
    "print(\"Confusion matrix (rows=true, cols=pred):\\n\", cm.cpu().numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f5e709f6-a56b-4c4c-b691-e17bf1acb3e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f0dee5cd550>]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(train_progress_matrix[0:epochs:5,0], color='royalblue')\n",
    "plt.plot(train_progress_matrix[0:epochs:5,1], color='lightseagreen')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "9829213e-7b84-4d35-ba15-f1489ed98de7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/raid1/xdchen/collaborations/Pei/NN_type/plots/fig01.NN_prediction.png\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x500 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig1 = plt.figure(figsize=(9,5))\n",
    "[axes1, axes2] = PlotFuncsXC.crt_subplot2grid_matrix([10,2,10], [1,0,1], [10,1,10,1,10,1,10], [1,0,1,0,1,0,1])\n",
    "axes_full = axes1 + axes2\n",
    "\n",
    "xdata = np.linspace(-1, 1, 201)\n",
    "\n",
    "for i in np.arange(4):\n",
    "    if i==2:\n",
    "        plotdata = X_val[y_val==i].numpy().T/2\n",
    "    else:\n",
    "        plotdata = X_val[y_val==i].numpy().T\n",
    "\n",
    "    axis = axes_full[i]\n",
    "    axis.plot(xdata, plotdata[:,10], color='black', linewidth=1, zorder=2)\n",
    "    axis.plot(xdata, plotdata[:,1::], color='lightgrey', linewidth=0.5, zorder=0)\n",
    "    axis.set_title('Type %d' % (i+1), loc='left', fontsize=10)\n",
    "    axis.set_title('n=%02d' % (plotdata.shape[1]), loc='right', fontsize=10)\n",
    "    axis.set_xlim([-1,1])\n",
    "    axis.set_ylim([-1,1])\n",
    "\n",
    "\n",
    "for axis in axes_full[0:4]:\n",
    "    axis.set_xticklabels([])\n",
    "for i in [1,2,5,6]:\n",
    "    axes_full[i].set_yticklabels([])\n",
    "for i in [3,7]:\n",
    "    axes_full[i].yaxis.tick_right()\n",
    "    axes_full[i].yaxis.set_label_position(\"right\")\n",
    "\n",
    "figname = plotdir + 'fig01.NN_prediction.png'\n",
    "print(figname)\n",
    "#fig1.savefig(figname, dpi=600)\n",
    "\n",
    "plt.show()\n",
    "plt.close()\n",
    "del(fig1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "fa4b923e-7d2b-4281-961d-9290376b8d7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "numstrs = ['0', 'I', 'VII', 'IV', 'IX', 'V', 'VI', 'VII', 'VIII'] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "462ca941-1d26-49ca-8354-eb55bf0e555a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/raid1/xdchen/collaborations/Pei/NN_type/plots/fig01.NN_prediction.group1.png\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig1 = plt.figure(figsize=(9,5))\n",
    "[axes1] = PlotFuncsXC.crt_subplot2grid_matrix([10,2,10], [1,0,0], [10,1,10,1,10,1,10], [1,0,1,0,1,0,1])\n",
    "axes_full = axes1\n",
    "\n",
    "xdata = np.linspace(-1, 1, 201)\n",
    "\n",
    "for i,axis in zip([0,2,1,3], axes_full):\n",
    "    if i==2:\n",
    "        plotdata = X_val[y_val==i].numpy().T/2\n",
    "    else:\n",
    "        plotdata = X_val[y_val==i].numpy().T\n",
    "\n",
    "    if i==0 or i==3:\n",
    "        axis.plot(xdata, plotdata[:,5], color='black', linewidth=2, zorder=2)\n",
    "    else:\n",
    "        axis.plot(xdata, plotdata[:,16], color='black', linewidth=2, zorder=2)        \n",
    "    axis.plot(xdata, plotdata[:,1::], color='lightgrey', linewidth=0.5, zorder=0)\n",
    "    axis.text(-0.95, 0.95, 'Type %s' % numstrs[(i+1)], ha='left', va='top', fontsize=10)\n",
    "    axis.text(0.95, -0.95, r'$N_{testing}$=%02d' % (plotdata.shape[1]), ha='right', va='bottom', fontsize=10)\n",
    "    axis.set_xlim([-1,1])\n",
    "    axis.set_ylim([-1.02,1.02])\n",
    "\n",
    "\n",
    "for axis in axes_full[0:4]:\n",
    "    axis.set_xticks([-1, 0, 1])\n",
    "    axis.set_yticks([-1, -0.5, 0, 0.5, 1])\n",
    "    axis.set_yticklabels([-1, -0.5, 0, 0.5, 1], fontsize=10)\n",
    "for i in [1,2]:\n",
    "    axes_full[i].set_yticklabels([])\n",
    "for i in [3]:\n",
    "    axes_full[i].yaxis.tick_right()\n",
    "    axes_full[i].yaxis.set_label_position(\"right\")\n",
    "\n",
    "figname = plotdir + 'fig01.NN_prediction.group1.png'\n",
    "print(figname)\n",
    "#fig1.savefig(figname, dpi=600)\n",
    "\n",
    "plt.show()\n",
    "plt.close()\n",
    "del(fig1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c309e65-56e9-4673-abc0-03c6e58e7fa3",
   "metadata": {},
   "source": [
    "## 3. real world sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "80150f33-0c46-48c1-940e-aaad0ee4345a",
   "metadata": {},
   "outputs": [],
   "source": [
    "infile = '/raid1/xdchen/collaborations/Pei/NN_type/data/data_blue.mat'\n",
    "data_blue_full = sio.loadmat(infile)['data_blue'][:,0]\n",
    "data_blue = data_blue_full[111:187]\n",
    "\n",
    "infile = '/raid1/xdchen/collaborations/Pei/NN_type/data/data_red.mat'\n",
    "data_red_full = sio.loadmat(infile)['data_red'][:,0]\n",
    "data_red = data_red_full[115:184]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "2b07ef1c-06f7-47d2-8263-6483502593ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_blue_rescale = np.zeros((1,201))\n",
    "data_blue_rescale[0] = np.interp(np.linspace(-1, 1, 201), np.linspace(-1, 1, 76), data_blue)\n",
    "data_blue_rescale[0] = data_blue_rescale/np.maximum(data_blue.max(), data_blue.min()*-1)\n",
    "#data_blue_rescale[0] = (data_blue_rescale-data_blue.min())/(data_blue.max()-data_blue.min())*2-1\n",
    "\n",
    "data_red_rescale = np.zeros((1,201))\n",
    "data_red_rescale[0] = np.interp(np.linspace(-1, 1, 201), np.linspace(-1, 1, 69), data_red)\n",
    "data_red_rescale[0] = data_red_rescale/np.maximum(data_red.max(), data_red.min()*-1)\n",
    "#data_red_rescale[0] = (data_red_rescale-data_red.min())/(data_red.max()-data_red.min())*2-1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "85686e6a-de56-4cd8-be41-29e287cff0cd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n"
     ]
    }
   ],
   "source": [
    "with torch.no_grad():\n",
    "    probs = torch.softmax(model(torch.tensor(data_blue_rescale, dtype=torch.float32).to(device)), dim=1)\n",
    "    preds = torch.argmax(probs, dim=1).cpu().numpy()\n",
    "    pred_blue = preds[0]+1\n",
    "    print(pred_blue)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "773ce7c3-f76a-4728-9f59-164862d7d96f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n"
     ]
    }
   ],
   "source": [
    "with torch.no_grad():\n",
    "    probs = torch.softmax(model(torch.tensor(data_red_rescale, dtype=torch.float32).to(device)), dim=1)\n",
    "    preds = torch.argmax(probs, dim=1).cpu().numpy()\n",
    "    pred_red = preds[0]+1\n",
    "    print(pred_red)\n",
    "\n",
    "pred_red = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "6e62399d-bec4-442f-9c8b-64a401105d84",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/raid1/xdchen/collaborations/Pei/NN_type/plots/fig03.NN_prediction.RealWorldSample.png\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x350 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig3 = plt.figure(figsize=(7,3.5))\n",
    "[[ax1,ax2]] = PlotFuncsXC.crt_subplot2grid_matrix([10,1], [1,0], [10,1,10], [1,0,1])\n",
    "\n",
    "ax1.scatter(np.linspace(-1, 1, 201), data_red_rescale[0], s=0.5, color='red')\n",
    "ax2.scatter(np.linspace(-1, 1, 201), data_blue_rescale[0], s=0.5, color='blue')\n",
    "\n",
    "# some guess\n",
    "ax1.plot(np.linspace(-1, 1, 201), (Xdata_full[ydata_full==(pred_red-1)].T)[:,85], linestyle='--', linewidth=1, color='black')\n",
    "#for i in np.arange(90):\n",
    "#    ax1.plot(np.linspace(-1, 1, 201), (Xdata_full[ydata_full==(pred_red-1)].T/2)[:,i], linestyle='--', linewidth=1, color='black')\n",
    "ax2.plot(np.linspace(-1, 1, 201), (Xdata_full[ydata_full==(pred_blue-1)].T/2)[:,25], linestyle='--', linewidth=1, color='black')\n",
    "\n",
    "for axis in [ax1,ax2]:\n",
    "    axis.set_xlim([-1,1])\n",
    "    axis.set_ylim([-1,1])\n",
    "    axis.set_xticks([-1, -0.5, 0, 0.5, 1])\n",
    "    axis.set_yticks([-1, -0.5, 0, 0.5, 1])\n",
    "    \n",
    "ax2.yaxis.tick_right()\n",
    "ax2.yaxis.set_label_position(\"right\")\n",
    "\n",
    "#for axis in [ax1, ax2]:\n",
    "#    axis.set_title('Step 2', loc='left', fontsize=12)\n",
    "#ax1.text(-0.95, 0.95, 'Sample #1', ha='left', va='top', fontsize=12)\n",
    "ax1.text(-0.95, 0.95, 'Identified type: %s' % numstrs[pred_red], ha='left', va='top', fontsize=12)\n",
    "#ax2.text(-0.95, 0.95, 'Sample #2', ha='left', va='top', fontsize=12)\n",
    "ax2.text(-0.95, 0.95, 'Identified type: %s' % numstrs[pred_blue], ha='left', va='top', fontsize=12)\n",
    "\n",
    "figname = plotdir + 'fig03.NN_prediction.RealWorldSample.png'\n",
    "print(figname)\n",
    "#fig3.savefig(figname, dpi=600)\n",
    "\n",
    "plt.show()\n",
    "plt.close()\n",
    "del(fig3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dde49824-6983-4677-b4aa-21a0c30cd675",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "py312-E2S",
   "language": "python",
   "name": "py312-e2s"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
